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Datasets:
mstz
/
adult

Tasks:
Tabular Classification
Modalities:
Tabular
Text
Languages:
English
Size:
100K - 1M
Tags:
adult
tabular_classification
binary_classification
multiclass_classification
UCI
Libraries:
Datasets
License:
Dataset card Viewer Files Community
mstz commited on
Commit
8443ee7
·
1 Parent(s): b42d454

Upload adult.py

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Files changed (1) hide show
  1. adult.py +52 -41
adult.py CHANGED
@@ -84,47 +84,58 @@ urls_per_split = {
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  "test": "https://huggingface.co/datasets/mstz/adult/raw/main/adult_ts.csv"
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  }
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  features_types_per_config = {
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- "income": {"age": datasets.Value("int64"),
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- "capital_gain": datasets.Value("float64"),
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- "capital_loss": datasets.Value("float64"),
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- "education": datasets.Value("int8"),
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- "final_weight": datasets.Value("int64"),
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- "hours_per_week": datasets.Value("int64"),
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- "marital_status": datasets.Value("string"),
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- "native_country": datasets.Value("string"),
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- "occupation": datasets.Value("string"),
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- "race": datasets.Value("string"),
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- "relationship": datasets.Value("string"),
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- "sex": datasets.Value("int8"),
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- "workclass": datasets.Value("string"),
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- "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
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- "income-no race": {"age": datasets.Value("int64"),
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- "capital_gain": datasets.Value("float64"),
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- "capital_loss": datasets.Value("float64"),
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- "education": datasets.Value("int64"),
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- "final_weight": datasets.Value("int64"),
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- "hours_per_week": datasets.Value("int64"),
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- "marital_status": datasets.Value("string"),
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- "native_country": datasets.Value("string"),
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- "occupation": datasets.Value("string"),
110
- "relationship": datasets.Value("string"),
111
- "sex": datasets.Value("int8"),
112
- "workclass": datasets.Value("string"),
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- "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
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- "race": {"age": datasets.Value("int64"),
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- "capital_gain": datasets.Value("float64"),
116
- "capital_loss": datasets.Value("float64"),
117
- "education": datasets.Value("int64"),
118
- "final_weight": datasets.Value("int64"),
119
- "hours_per_week": datasets.Value("int64"),
120
- "marital_status": datasets.Value("string"),
121
- "native_country": datasets.Value("string"),
122
- "occupation": datasets.Value("string"),
123
- "relationship": datasets.Value("string"),
124
- "sex": datasets.Value("int8"),
125
- "workclass": datasets.Value("string"),
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- "over_threshold": datasets.Value("int8"),
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- "race": datasets.ClassLabel(num_classes=5, names=["White", "Black", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other"])}
 
 
 
 
 
 
 
 
 
 
 
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  }
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  features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
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  "test": "https://huggingface.co/datasets/mstz/adult/raw/main/adult_ts.csv"
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  }
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  features_types_per_config = {
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+ "encoding": {
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+ "feature": datasets.Value("string"),
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+ "original_value": datasets.Value("string"),
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+ "encoded_value": datasets.Value("int64"),
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+ },
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+ "income": {
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+ "age": datasets.Value("int64"),
94
+ "capital_gain": datasets.Value("float64"),
95
+ "capital_loss": datasets.Value("float64"),
96
+ "education": datasets.Value("int8"),
97
+ "final_weight": datasets.Value("int64"),
98
+ "hours_per_week": datasets.Value("int64"),
99
+ "marital_status": datasets.Value("string"),
100
+ "native_country": datasets.Value("string"),
101
+ "occupation": datasets.Value("string"),
102
+ "race": datasets.Value("string"),
103
+ "relationship": datasets.Value("string"),
104
+ "sex": datasets.Value("int8"),
105
+ "workclass": datasets.Value("string"),
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+ "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
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+ },
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+ "income-no race": {
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+ "age": datasets.Value("int64"),
110
+ "capital_gain": datasets.Value("float64"),
111
+ "capital_loss": datasets.Value("float64"),
112
+ "education": datasets.Value("int64"),
113
+ "final_weight": datasets.Value("int64"),
114
+ "hours_per_week": datasets.Value("int64"),
115
+ "marital_status": datasets.Value("string"),
116
+ "native_country": datasets.Value("string"),
117
+ "occupation": datasets.Value("string"),
118
+ "relationship": datasets.Value("string"),
119
+ "sex": datasets.Value("int8"),
120
+ "workclass": datasets.Value("string"),
121
+ "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
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+ },
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+ "race": {
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+ "age": datasets.Value("int64"),
125
+ "capital_gain": datasets.Value("float64"),
126
+ "capital_loss": datasets.Value("float64"),
127
+ "education": datasets.Value("int64"),
128
+ "final_weight": datasets.Value("int64"),
129
+ "hours_per_week": datasets.Value("int64"),
130
+ "marital_status": datasets.Value("string"),
131
+ "native_country": datasets.Value("string"),
132
+ "occupation": datasets.Value("string"),
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+ "relationship": datasets.Value("string"),
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+ "sex": datasets.Value("int8"),
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+ "workclass": datasets.Value("string"),
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+ "over_threshold": datasets.Value("int8"),
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+ "race": datasets.ClassLabel(num_classes=5, names=["White", "Black", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other"])
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+ }
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  }
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  features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
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